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2017-10-20Zeitschriftenartikel DOI: 10.3389/fpsyg.2017.01849
Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data
dc.contributor.authorde Haan-Rietdijk, Silvia
dc.contributor.authorVoelkle, Manuel C.
dc.contributor.authorKeijsers, Loes
dc.contributor.authorHamaker, Ellen L.
dc.date.accessioned2020-03-02T11:55:04Z
dc.date.available2020-03-02T11:55:04Z
dc.date.issued2017-10-20none
dc.date.updated2019-10-27T16:44:41Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/21983
dc.description.abstractThe Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectexperience sampling methodeng
dc.subjectautoregressive modelingeng
dc.subjectcontinuous-timeeng
dc.subjectdiscrete-timeeng
dc.subjectunequal spacingeng
dc.subjectintensive longitudinal dataeng
dc.subjecttime series analysiseng
dc.subject.ddc150 Psychologienone
dc.titleDiscrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Datanone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/21983-4
dc.identifier.doi10.3389/fpsyg.2017.01849none
dc.identifier.doihttp://dx.doi.org/10.18452/21242
dc.type.versionpublishedVersionnone
local.edoc.container-titleFrontiers in Psychologynone
local.edoc.pages19none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionLebenswissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameFrontiers Media S.A.none
local.edoc.container-publisher-placeLausannenone
local.edoc.container-volume8none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber1849none
dc.identifier.eissn1664-1078
local.edoc.affiliationde Haan-Rietdijk, Silvia; Methodology and Statistics for the Behavioural, Biomedical and Social Sciences, Utrecht University, Utrecht, Netherlandsnone
local.edoc.affiliationVoelkle, Manuel C.; Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germanynone
local.edoc.affiliationKeijsers, Loes; Developmental Psychology, Tilburg University, Tilburg, Netherlandsnone
local.edoc.affiliationHamaker, Ellen L.; Methodology and Statistics for the Behavioural, Biomedical and Social Sciences, Utrecht University, Utrecht, Netherlandsnone

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